Multi-objective optimization of diesel engine performance and emission using grasshopper optimization algorithm

Abstract

A recently invented algorithm called the grasshopper optimization algorithm (GOA) was used to predict and optimize palm oil biodiesel operated in a diesel engine. The work was conducted in three stages: (i) designing an experiment and performing the experiments, (ii) mathematical modeling, and (iii) optimization using GOA. By using regression modeling over these experimental results, the mathematical equations between the factors (biodiesel ratio (%) and load (Nm)) and the responses (BTE, BSFC, BSCO, BSNOx, BSCO2, BSHC, and Smoke) were calculated. The results showed that the factors used in the model were sufficient to explain the change in the response, and no additional factors in the mathematical models were required. The ANOVA results showed that the p-value for all the regression models were 0.000 < 0.05, which indicated their significance. Moreover, the regression models best fit the given observations with a low prediction error. The three confirmation tests also revealed satisfying results with low errors. The range of prediction error for BTE, BSFC, BSCO, BSNOx, BSCO2, BSHC, and Smoke were 0.25–3.00%, 2.55–8.20%, 4.61–11.65%, 1.71–12.20%, 1.35–3.52%, 0.02–7.75%, and 0.69–4.34%, respectively. The optimized operating conditions for the maximum engine performance and the minimum emissions was given by 50% biodiesel run at 7 Nm engine load.